{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- <center> -->\n",
    "<h1> Using MOMENT for Anomaly Detection </h1>\n",
    "<!-- </center> -->\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "### 1. A Quick Introduction to Anomaly Detection\n",
    "### 2. Loading MOMENT\n",
    "### 3. Inputs and Outputs\n",
    "### 4. Zero-Shot Anomaly Detection\n",
    "#### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4.1 Anomaly Detection using MOMENT\n",
    "#### &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4.1 Results\n",
    "### 5. 5. Example Code to Fine-tune MOMENT for Anomaly Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. A Quick Introduction to Anomaly Detection\n",
    "\n",
    "Detecting anomalous subsequences within time series data is important across various domains, spanning from manufacturing processes to financial applications and healthcare monitoring. An anomaly within a time series can indicate crucial events such as production faults, delivery bottlenecks, system defects, or irregular heart rhythms, making anomaly detection a focal point of interest. Given the large size and often intricate patterns exhibited by time series data, there is substantial interest in developing machine learning models to automatically identify anomalies. In this tutorial, we will explore how we can use MOMENT to detect anomalies in a zero-shot setting! Below, we mathematically describe the anomaly detection problem:\n",
    "\n",
    "**Problem**: Given a time-series $T = [x_1, ..., x_L], \\ x_i \\in \\mathbb{R}^{C}$ of length $L$ with $C$ channels (sensors or variables) and a anolmaly criterion $C$, the Anomaly-Detection problem is to predict the anomaly score at each timestamp of the time-series $C = [C_1, ..., C_L]$. \n",
    "\n",
    "In this tutorial, we will use MOMENT to reconstruct an input time series. Since MOMENT is pre-trained on millions of timesteps of time series data, we expect it to model \"normal behavior\". Therefore, we will consider timesteps where the observed values and MOMENT's predictions are signficantly different to be anomalies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading MOMENT\n",
    "\n",
    "We will first install the MOMENT package, load some essential packages and the pre-trained model. \n",
    "\n",
    "MOMENT can be loaded in 4 modes: (1) `reconstruction`, (2) `embedding`, (3) `forecasting`, and (4) `classification`.\n",
    "\n",
    "In the `reconstruction` mode, MOMENT reconstructs input time series, potentially containing missing values. We can solve imputation and anomaly detection problems in this mode. This mode is suitable for solving imputation and anomaly detection tasks. During pre-training, MOMENT is trained to predict the missing values within uniformly randomly masked patches (disjoint sub-sequences) of the input time series, leveraging information from observed data in other patches. As a result, MOMENT comes equipped with a pre-trained reconstruction head, enabling it to address imputation and anomaly detection challenges in a zero-shot manner! Check out the `imputation.ipynb` notebook for more details!\n",
    "\n",
    "In the `embedding` model, MOMENT learns a $d$-dimensional embedding (e.g., $d=1024$ for `MOMENT-1-large`) for each input time series. These embeddings can be used for clustering and classification. MOMENT can learn embeddings in a zero-shot setting! Check out `representation_learning.ipynb` notebook for more details! \n",
    "\n",
    "The `forecasting` and `classification` modes are used for forecasting and classification tasks, respectively. In these modes, MOMENT learns representations which are subsequently mapped to the forecast horizon or the number of classes, using linear forecasting and classification heads. Both the forecasting and classification head are randomly initialized, and therefore must be fine-tuned before use. Check out the `forecasting.ipynb` notebook for more details!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install numpy pandas scikit-learn matplotlib tqdm\n",
    "!pip install git+https://github.com/moment-timeseries-foundation-model/moment.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from momentfm import MOMENTPipeline\n",
    "\n",
    "model = MOMENTPipeline.from_pretrained(\n",
    "    \"AutonLab/MOMENT-1-large\", \n",
    "    model_kwargs={\"task_name\": \"reconstruction\"}, # For anomaly detection, we will load MOMENT in `reconstruction` mode\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MOMENTPipeline(\n",
      "  (normalizer): RevIN()\n",
      "  (tokenizer): Patching()\n",
      "  (patch_embedding): PatchEmbedding(\n",
      "    (value_embedding): Linear(in_features=8, out_features=1024, bias=False)\n",
      "    (position_embedding): PositionalEmbedding()\n",
      "    (dropout): Dropout(p=0.1, inplace=False)\n",
      "  )\n",
      "  (encoder): T5Stack(\n",
      "    (embed_tokens): Embedding(32128, 1024)\n",
      "    (block): ModuleList(\n",
      "      (0): T5Block(\n",
      "        (layer): ModuleList(\n",
      "          (0): T5LayerSelfAttention(\n",
      "            (SelfAttention): T5Attention(\n",
      "              (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (relative_attention_bias): Embedding(32, 16)\n",
      "            )\n",
      "            (layer_norm): T5LayerNorm()\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "          )\n",
      "          (1): T5LayerFF(\n",
      "            (DenseReluDense): T5DenseGatedActDense(\n",
      "              (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
      "              (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
      "              (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
      "              (dropout): Dropout(p=0.1, inplace=False)\n",
      "              (act): NewGELUActivation()\n",
      "            )\n",
      "            (layer_norm): T5LayerNorm()\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1-23): 23 x T5Block(\n",
      "        (layer): ModuleList(\n",
      "          (0): T5LayerSelfAttention(\n",
      "            (SelfAttention): T5Attention(\n",
      "              (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "              (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
      "            )\n",
      "            (layer_norm): T5LayerNorm()\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "          )\n",
      "          (1): T5LayerFF(\n",
      "            (DenseReluDense): T5DenseGatedActDense(\n",
      "              (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
      "              (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
      "              (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
      "              (dropout): Dropout(p=0.1, inplace=False)\n",
      "              (act): NewGELUActivation()\n",
      "            )\n",
      "            (layer_norm): T5LayerNorm()\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (final_layer_norm): T5LayerNorm()\n",
      "    (dropout): Dropout(p=0.1, inplace=False)\n",
      "  )\n",
      "  (head): PretrainHead(\n",
      "    (dropout): Dropout(p=0.1, inplace=False)\n",
      "    (linear): Linear(in_features=1024, out_features=8, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model.init()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Inputs and Outputs\n",
    "\n",
    "Let's begin by performing a forward pass through MOMENT and examining its outputs!\n",
    "\n",
    "MOMENT takes 3 inputs: \n",
    "1. An input time series of length $T=512$ timesteps and $C$ channels, and \n",
    "2. Two optional masks, both of length $T=512$. \n",
    "    - The input mask is utilized to regulate the time steps or patches that the model should attend to. For instance, in the case of shorter time series, you may opt not to attend to padding. To implement this, you can provide an input mask with zeros in the padded locations.  \n",
    "    - The second mask, referred to simply as mask, denotes masked or unobserved values. We employ mask tokens to replace all patches containing any masked time step (for further details, refer to Section 3.2 in our [paper](https://arxiv.org/abs/2402.03885)). MOMENT can attend to these mask tokens during reconstruction.\n",
    "    - By default, all time steps are observed and attended to.\n",
    "\n",
    "MOMENT returns a `TimeseriesOutputs` object. Since this is an anomaly detection task (via reconstruction), it returns a `reconstruction` of the input. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimeseriesOutputs(forecast=None,\n",
      "                  anomaly_scores=None,\n",
      "                  logits=None,\n",
      "                  labels=None,\n",
      "                  input_mask=tensor([[1., 1., 1.,  ..., 1., 1., 1.],\n",
      "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "        ...,\n",
      "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "        [1., 1., 1.,  ..., 1., 1., 1.],\n",
      "        [1., 1., 1.,  ..., 1., 1., 1.]]),\n",
      "                  pretrain_mask=tensor([[1, 1, 1,  ..., 1, 1, 1],\n",
      "        [1, 1, 1,  ..., 1, 1, 1],\n",
      "        [1, 1, 1,  ..., 1, 1, 1],\n",
      "        ...,\n",
      "        [1, 1, 1,  ..., 1, 1, 1],\n",
      "        [1, 1, 1,  ..., 1, 1, 1],\n",
      "        [1, 1, 1,  ..., 1, 1, 1]]),\n",
      "                  reconstruction=tensor([[[-0.4326, -0.0660, -0.1953,  ...,  0.4186, -0.2626,  0.3695]],\n",
      "\n",
      "        [[-0.1385,  0.0261,  0.0552,  ...,  0.1251,  0.3459,  0.0109]],\n",
      "\n",
      "        [[-0.0557,  0.0579,  0.1639,  ...,  0.1815,  0.3360,  0.4162]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.1341,  0.0905,  0.0176,  ...,  0.3540, -0.1382, -0.0545]],\n",
      "\n",
      "        [[ 0.1778,  0.0108,  0.1157,  ...,  0.3781,  0.0140,  0.4490]],\n",
      "\n",
      "        [[-0.1621,  0.0992, -0.1335,  ...,  0.3331, -0.2685,  0.1848]]],\n",
      "       grad_fn=<AddBackward0>),\n",
      "                  embeddings=None,\n",
      "                  metadata=None,\n",
      "                  illegal_output=None)\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "import torch\n",
    "\n",
    "# takes in tensor of shape [batchsize, n_channels, context_length]\n",
    "x = torch.randn(16, 1, 512)\n",
    "output = model(x)\n",
    "pprint(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Zero-Shot Anomaly Detection\n",
    "\n",
    "Now we'll show you how to use MOMENT to do zero-shot anomaly detection!\n",
    "\n",
    "In these experiments, we will use the tiltAPB2 dataset from the [UCR Anomaly Archive](https://www.cs.ucr.edu/%7Eeamonn/time_series_data_2018/). The waveform is the Arterial blood pressure (ABP) of a healthy male on a tilt table. [Wu et al., 2021](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9537291) introduced a synthetic anomaly such that the direction of the time series is reversed for about 4 beats.\n",
    "\n",
    "We'll start by reading and pre-processing this dataset using the `AnomalyDetectionDataset` class. Since we can do zero-shot anomaly detection, we will just load the testing part of this data. Note that MOMENT was not exposed to the testing part of this dataset during pre-training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from momentfm.data.anomaly_detection_dataset import AnomalyDetectionDataset\n",
    "\n",
    "test_dataset = AnomalyDetectionDataset(data_split='test', random_seed=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "idx = np.random.randint(0, len(test_dataset))\n",
    "plt.plot(test_dataset[idx][0].squeeze(), c='darkblue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Anomaly Detection using MOMENT\n",
    "Now we will use MOMENT to compute anomaly scores for the time series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=False, drop_last=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:22<00:00,  7.65s/it]\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from tqdm import tqdm\n",
    "\n",
    "model = model.to(\"cuda\").float()\n",
    "\n",
    "trues, preds, labels = [], [], []\n",
    "with torch.no_grad():\n",
    "    for batch_x, batch_masks, batch_labels in tqdm(test_dataloader, total=len(test_dataloader)):\n",
    "        batch_x = batch_x.to(\"cuda\").float()\n",
    "        batch_masks = batch_masks.to(\"cuda\")\n",
    "\n",
    "        output = model(batch_x, input_mask=batch_masks) # [batch_size, n_channels, window_size]\n",
    "\n",
    "        trues.append(batch_x.detach().squeeze().cpu().numpy())\n",
    "        preds.append(output.reconstruction.detach().squeeze().cpu().numpy())\n",
    "        labels.append(batch_labels.detach().cpu().numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Results\n",
    "\n",
    "We will use adjusted best F1, a metric which is frequently used in practice [Goswami et al., 2023](https://arxiv.org/abs/2210.01078), to evaluate the anomaly detection performance. MOMENT uses the mean squarred error between its predictions and the observed time series as an anomaly score. To convert anomaly score to binary predictions, we must find a threshold, such that time steps with an anomaly score exceeding this threshold are considered anomalous. Adjusted best F1 finds the best threshold which maximizes the F1 of the anomaly detection model. To account for the temporal nature of this problem, a model is said to have correctly identified the complete anomaly sequence as long as it detects any anomalous timestep.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "trues = np.concatenate(trues, axis=0).flatten()\n",
    "preds = np.concatenate(preds, axis=0).flatten()\n",
    "labels = np.concatenate(labels, axis=0).flatten()\n",
    "\n",
    "# The last and the second to last windows have overlapping timesteps. We will remove these overlapping predictions\n",
    "n_unique_timesteps = 512 - trues.shape[0] + test_dataset.length_timeseries\n",
    "trues = np.concatenate([trues[:512*(test_dataset.length_timeseries//512)], trues[-n_unique_timesteps:]])\n",
    "preds = np.concatenate([preds[:512*(test_dataset.length_timeseries//512)], preds[-n_unique_timesteps:]])\n",
    "labels = np.concatenate([labels[:512*(test_dataset.length_timeseries//512)], labels[-n_unique_timesteps:]])\n",
    "assert trues.shape[0] == test_dataset.length_timeseries\n",
    "\n",
    "# We will use the Mean Squared Error (MSE) between the observed values and MOMENT's predictions as the anomaly score\n",
    "anomaly_scores = (trues - preds)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot Adjusted Best F1 Score: 0.9689099495033109\n"
     ]
    }
   ],
   "source": [
    "from momentfm.utils.anomaly_detection_metrics import adjbestf1\n",
    "\n",
    "print(f\"Zero-shot Adjusted Best F1 Score: {adjbestf1(y_true=labels, y_scores=anomaly_scores)}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anomaly_start = 74158\n",
    "anomaly_end = 74984\n",
    "start = anomaly_start-512\n",
    "end = anomaly_end+512\n",
    "\n",
    "plt.plot(trues[start:end], label=\"Observed\", c='darkblue')\n",
    "plt.plot(preds[start:end], label=\"Predicted\", c='red')\n",
    "plt.plot(anomaly_scores[start:end], label=\"Anomaly Score\", c='black')\n",
    "plt.legend(fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the figure, the anomaly score is higher around the anomaly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Example Code to Fine-tune MOMENT for Anomaly Detection\n",
    "\n",
    "To improve MOMENT's anomaly performance, you can fine-tune it on any dataset. In our [paper](https://arxiv.org/abs/2402.03885), we fine-tune the final reconstruction head, but you can also fine-tune the entire model on your data. Here is some example code:\n",
    "\n",
    "```python\n",
    "\n",
    "# Optimize Mean Squarred Error using your favourite optimizer\n",
    "criterion = torch.nn.MSELoss() \n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "\n",
    "mask_generator = Masking(mask_ratio=0.3) # Mask 30% of patches randomly \n",
    "\n",
    "for batch_x, batch_masks in tqdm(test_dataloader, total=len(test_dataloader)):\n",
    "    n_channels = batch_x.shape[1]\n",
    "    \n",
    "    # Reshape to [batch_size * n_channels, 1, window_size]\n",
    "    batch_x = batch_x.reshape((-1, 1, 512)) \n",
    "    \n",
    "    batch_masks = batch_masks.to(device).long()\n",
    "    batch_masks = batch_masks.repeat_interleave(n_channels, axis=0)\n",
    "    \n",
    "    # Randomly mask some patches of data\n",
    "    mask = mask_generator.generate_mask(\n",
    "        x=batch_x, input_mask=batch_masks).to(device).long()\n",
    "\n",
    "    # Forward\n",
    "    output = model(batch_x, input_mask=batch_masks, mask=mask) \n",
    "    \n",
    "    # Compute loss\n",
    "    loss = criterion(output.reconstruction, original)\n",
    "        \n",
    "    print(f\"loss: {loss.item()}\")\n",
    "    \n",
    "    # Backward\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "```"
   ]
  }
 ],
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